IJCER (www.ijceronline.com) International Journal of computational Engineering research

International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 5

Performance analysis of Energy detection, Matched filter detection &
Cyclostationary feature detection Spectrum Sensing Techniques
Mr. Pradeep Kumar Verma1, Mr. Sachin Taluja2, Prof. Rajeshwar Lal Dua3
1
M.Tech Scholar, Department of Electronics & Communication Engineering, Jaipur National University, Jaipur, India
2
M.Tech Scholar, Department of Electronics & Communication Engineering, Jaipur National University, Jaipur, India
3
HOD, Electronics & Communication Engineering, Jaipur National University, Jaipur, India.

Abstract- The growing demand of wireless applications has put a lot of constraints on the usage of available radio
spectrum which is limited and precious resource. However, a fixed spectrum assignment has lead to under utilization of
spectrum as a great portion of licensed spectrum is not effectively utilized. Cognitive radio is a promising technology
which provides a novel way to improve utilization efficiency of available electromagnetic spectrum. Spectrum sensing
helps to detect the spectrum holes (underutilized bands of the spectrum) providing h i g h s p e c t r a l r e s o l u t i o n
capability. T h i s i s a r e v i e w p a p e r that compares the performance of three main spectrum s e n s i n g techniques.

Keywords- Cognitive Radio (CR), Energy Detection (ED), Matched Filter Detection (MFD), Cyclostationary feature
Detection.

I. Introduction
The available radio spectrum is limited and it is getting crowded day by day as there is increase in the number of wireless
devices and applications. In the studies it has been found that the allocated radio spectrum is underutilized because it has
been statistically allocated not dynamically (allocated when needed). Also the approach of radio spectrum management is
not flexible, since, each wireless operator is assigned a license to operate in a certain frequency band. In the present
scenario, it has been found out that these allocated radio spectrums are free 15% to 85% most of the time i.e. they are
inefficiently used depending upon the geographical area. Since most of the useful radio spectrum already allocated, it is
difficult to find vacant frequency bands to either deploy new services or to enhance the existing ones. In order to overcome
this situation, we need to come up with a means for improved utilization of the spectrum creating opportunities for dynamic
spectrum access. [1]-[3].
The issue of spectrum underutilization in wireless communication can be solved in a better way using Cognitive Radio.
Cognitive Radio is characterized by the fact that it can adapt according to the environment by changing its
transmitting parameters, such as modulation, frequency, frame format, etc. T he main challenges with cognitive
radios are that it should not interfere with the licensed users and should vacate the band when required. For this
it should sense the signals faster. This work focuses on the spectrum sensing techniques that are based on primary
transmitter detection. In this category, three major spectrum sensing techniques “energy detection”, “matched filter
detection”, and “cyclostationary feature detection” are addressed. This paper involves the comparative analysis of these
spectrum sensing techniques for efficient working of cognitive radios.

II. Cognitive Radio
The concept behind the Cognitive users is that they have the ability to continuously sense the licensed
spectrum to search the unused locations, once the hollow locations are identified; Cognitive radio users utilize
those locations for transmission without interrupting the primary users. [4].The primary aim of a cognitive radio
system is to get hold of a best available channel by using the cognitive capability and re-configurability. Cognitive
capability is defined as the capacity of the radio system to gather information from the surroundings [5]. It
requires very complex or sophisticated techniques in order to observe the sudden variations and changes in the
radio environment without interfering with the existing users. Cognitive capability plays a major role to identify
the unused or white spaces in the frequency spectrum at a particular time so as to select a suitable spectrum
along with the suitable operating parameters. These unused channels are called spectrum holes or white spaces
[5]. The cognitive radio enables the use of white spaces in the spectrum that become available temporarily. As soon
as the primary user returns to its band, the cognitive user switches to a different spectrum hole or may stay in
the same band but alters the power level and modulation method for avoiding interference to the existing
licensed users in that band.

III. Spectrum Sensing
A major challenge in cognitive radio is that the secondary users need to detect the presence of primary users in a
licensed spectrum and quit the frequency band as quickly as possible if the corresponding primary radio emerges in order
to avoid interference to primary users. This technique is called spectrum sensing. Spectrum sensing and estimation is the
first step to implement Cognitive Radio system [6].
Issn 2250-3005(online) September| 2012 Page 1296


Energy detection Spectrum Sensing
It is a non coherent detection method that detects the primary signal based on the sensed energy [1]. Due to its
simplicity and no requirement of a priori knowledge of primary user signal, energy detection (ED) is the most popular
sensing technique in cooperative sensing [7][8][9].
The block diagram for the energy detection technique is shown in the Figure 1. In this method, signal is passed
through band pass filter of the bandwidth W and is integrated over time interval. The output from the integrator block
is then compared to a predefined threshold. This comparison is used to discover the existence of absence of the primary
user. The threshold value can set to be fixed or variable based on the channel conditions.
The ED is said to be the Blind signal detector because it ignores the structure of the signal. It estimates the presence
of the signal by comparing the energy received with a known threshold ν derived from the statistics of the noise.
Analytically, signal detection can be reduced to a simple identification problem, formalized as a hypothesis test,

y(k ) = n(k )........................H 0

y(k ) = h ∗ s(k ) + n(k ).........H 1 (1)

Where y(k) is the sample to be analyzed at each instant k and n (k) is the noise of variance σ2 . Let y(k) be a sequence
of received samples k Є {1, 2….N} at the signal detector, then a decision rule can be stated as,

H 0 ....if ε < v

H 1 ....if ε > v (2)
2
Where ε=|E y(k)|
The estimated energy of the received signal and v is chosen to be the noise variance σ2 [10].

Fig. 1: Block diagram of Energy detection [1].

The “probability of primary user detection” and the “probability of false detection” for the energy detection
method can be calculated by the given equations [10]:

(3)

Where λ= SNR,
n= TW (Time bandwidth product)
⎡(.)= complete gamma function,
⎡(.,.)= incomplete gamma function,
Qm= Generalized Marcum function [11].

Matched Filter Spectrum Detection
A matched filter (MF) is a linear filter designed to maximize the output signal to noise ratio for a given input signal.
When secondary user has a priori knowledge of primary user signal, matched filter detection is applied. Matched filter
operation is equivalent to correlation in which the unknown signal is convolved with the filter whose impulse response is
the mirror and time shifted version of a reference signal. The operation of matched filter detection is expressed as:

∞
Y[n] =∑h[n − k]x[k ] (3)
k=-∞



Where ‘x’ is the unknown signal (vector) and is convolved with the ‘h’, the impulse response of matched filter that is
matched to the reference signal for maximizing the SNR. Detection by using matched filter is useful only in cases where
the information from the primary users is known to the cognitive users [10].

Fig. 2: Block diagram of matched Filter detection [1].

Cyclostationary feature Spectrum Detection
It exploits the periodicity in the received primary signal to identify the presence of primary users (PU). The periodicity
is commonly embedded in sinusoidal carriers, pulse trains, spreading code, hopping sequences or cyclic prefixes of the
primary signals. Due to the periodicity, these cyclostationary signals exhibit the features of periodic statistics and
spectral correlation, which is not found in stationary noise and interference [ 11].
Thus, cyclostationary feature detection is robust to noise uncertainties and performs better than energy detection in low
SNR regions. Although it requires a priori knowledge of the signal characteristics, cyclostationary feature detection is
capable of distinguishing the CR transmissions from various types of PU signals. This eliminates the synchronization
requirement of energy detection in cooperative sensing. Moreover, CR users may not be required to keep silent during
cooperative sensing and thus improving the overall CR throughput. This method has its own shortcomings owing to its
high computational complexity and long sensing time. Due to these issues, this detection method is less common than
energy detection in cooperative sensing [12].

Fig. 3: Block diagram of Cyclostationary feature detection [1].

Process flow diagrams

Fig. 4: Process flow diagram of Energy Detection. Fig. 5: Process flow diagram of Matched filter Detection.



Fig. 6: Flow diagram of Cyclostationary feature Detection.

IV. Results And Analysis
An extensive set of simulations have been conducted using the system model as described in the previous
section. T he emphasis is to analyze the comparative performance of three spectrum sensing techniques. The
performance metrics used for comparison include the “probability of primary user detection” and “probability
of false detection”. The number of channels and the number primary users considered in this analysis is
twenty five and respectively. The SNR of the channels is considered to be precisely same and the channel
model is A W G N with zero mean. The results are shown in Figure-7 and Figure-8.

Probability of Primary Detection
Figure-7 depicts the “probability of primary user detection” as a function of SN R for the three cases: (i) energy
detection, (ii) matched filter detection and (iii) cyclo-stationary feature detection.
It is observed that for energy detection and matched filter detection, much higher SNR is required to obtain a
performance comparable to cyclostationary feature detection. For energy detection, about 16 dB s higher SNR is
needed to achieve 100% probability of detection whereas for matched filter detection, about 24 dB s higher
SNR is required. For cyclostationary feature detection, 100% probability of detection is attained at -8 dB s.
Cyclostationary feature detection performs well for very low SNR, however the major disadvantage is that it
requires large observation time for occupancy detection. Matched filter detection performs well as compared to
energy detection but restriction lies in prior knowledge of user signaling. Further, cyclostationary feature
detection algorithm is complex as compared to other detection techniques.

Probability of False Detection
Figure-8 illustrates the “probability of false detection” for three transmitter detection based spectrum sensing
techniques versus SNR.
It is observed that “probability of false detection” of cyclostationary feature detection is much smaller as
compared to other two techniques. In fact, it is zero for the range of SNR considered in this study i.e., -30 dB
to + 30 dB s. It is further seen that the “probability of false detection” for energy detection technique is inversely
proportional to the SNR. At low SNR we have higher probability of false detection and at high SNR we have
lower probability o f false detection, because energy detection cannot isolate between signal and noise. T he
probability of false detection for energy detection and matched filter detection approaches zero at about +14
dB s and +8 dB s respectively.



Fig. 7: Probability of Primary Detection.

Figure 8: Probability of False Detection.
I. Conclusion
To efficiently utilize the wireless spectrum cognitive radios were introduced which opportunistically utilize the holes
present in the spectrum. The most essential aspect of a cognitive radio system is spectrum sensing and various sensing
techniques which it uses to sense the spectrum. In this paper the main focus was on Energy Detection, Matched Filter
Detection and Cyclostationary feature Detection spectrum sensing techniques. The advantage of Energy detection is
that, it does not require any prior knowledge about primary users. It does not perform well at low SNR values, it
requires a minimum SNR for its working. The result in the paper shows that Energy detection starts working at -7 dB
s of SNR. Matched filter detection is better than energy detection as it starts working at low SNR of -30 dB s.
Cyclostationary feature detection is better than both the previous detection techniques since it produces better results
at lowest SNR, i.e. for values below -30 dB s. the results shows that the performance of energy detection gets better
with increasing SNR as the “probability of primary detection” increases from zero at -14 dB s to 100% at +8 dB s and
correspondingly the “probability of false detection” improves from 100% to zero. Similar type of performance is
achieved using matched filter detection as “probability of primary detection” and the “probability of false detection”
shows improvement in SNR as it varies from -30 dB s to +8 dB s. the cyclostationary feature detection outclasses the
other two sensing techniques as 100% “probability of primary detection” and zero “probability of false detection” is
achieved at -8 dB s, but the processing time of cyclostationary feature detection is greater than the energy dete ction
and matched filter detection techniques.

References
[1] Shahzad A. et. al. (2010), “Comparative Analysis of Primary Transmitter Detection Based Spectrum Sensing
Techniques in Cognitive Radio Systems,’’ Australian Journal of Basic and Applied Sciences,4(9), pp: 4522- 531,
INSInet Publication.
[2] Weifang Wang (2009), “Spectrum Sensing for Cognitive Radio’’, Third International Symposium on Intelligent
Information Technology Application Workshops, pp: 410- 12.
[3] V. Stoianovici, V. Popescu, M. Murroni (2008), “A Survey on spectrum sensing techniques in cognitive radio”
Bulletin of the Transilvania University of Braşov, Vol. 15 (50).
[4] Mitola, J. and G.Q. Maguire, 1999 “Cognitive Radio: Making Software R adios More Personal,” IEEE
Personal Communication Magazine, 6(4): 13-18.
[5] Haykin, S., 2005. "Cognitive Radio: B rain Empowered Wireless Communications", IEEE Journal on Selected
Areas in Communications, pp: 201-220.
[6] D. B. Rawat, G. Yan, C. Bajracharya (2010), “Signal Processing Techniques for Spectrum Sensing in Cognitive
Radio Networks’’, International Journal of Ultra Wideband Communications and Systems, Vol. x, No. x/x, pp:1-
10.



[7] Ekram Hossain, Vijay Bhargava (2007), “Cognitive Wireless Communication Networks”, Springer.
[8] Linda Doyle (2009), “Essentials of Cognitive Radio”, Cambridge University Press. [9] D. Cabric, A. Tkachenko,
and R. Brodersen, (2006) “Spectrum sensing measurements of pilot, energy and collaborative detection,” in Proc.
IEEE Military Commun. Conf., Washington, D.C., USA, pp: 1-7.
[10] Ian F. Akyildiz, Brandon F. Lo, Ravikumar (2011), “Cooperative spectrum sensing in cognitive radio networks: A
survey, Physical Communication”, pp: 40-62.
[11] A. Tkachenko, D. Cabric, and R. W. Brodersen, (2007), “Cyclostationary feature detector experiments using
reconfigurable BEE2,” in Proc. IEEE Int. Symposium on New Frontiers in Dynamic Spectrum Access Networks,
Dublin, Ireland, Apr, pp: 216-219.
[12] R. Tandra and A. Sahai (2007), “SNR walls for feature detectors”, in Proc. IEEE Int. Symposium on New
Frontiers in Dynamic Spectrum Access Networks, Dublin, Ireland, Apr, pp: 559–570.
[13] Parikshit Karnik and Sagar Dumbre (2004), "Transmitter Detection Techniques for Spectrum Sensing in
CR Networks", Department of Electrical and Computer Engineering Georgia Institute of Technology.

Authors

Pradeep Kumar Verma- Student of M. Tech (Communication and signal processing) final semester at Jaipur
National University, Jaipur. Completed B. Tech from Northern India Engineering College, Lucknow from Uttar
Pradesh Technical University in Electronics and Communications Engineering in 2009. Worked as Site Engineer
for 9 months in Telecom Industry. I have keen interest in subjects like signal and systems, digital communications,
information theory and coding and wireless communications.

Sachin Taluja M.Tech Scholar at Jaipur National University, Jaipur. He received B.E. from M.D.University Rohtak,
Haryana in Electronics and Communication . He has over 5 years of Industrial experience in the Field of Computers. His
Area of interest includes Network Security, Artificial intelligence, Communication system, Computer architecture, Wireless
Communications, Digital Communications, fiber optics, Nano Technology. He has attended various workshops on different
domains of computers.

Professor Rajeshwar Lal Dua – He is a fellow life member of IETE and also a life member of I.V.S & I.P.A, former -
scientist F of the Central Electronics Engineering Research Institute (CEERI), Pilani. Has been one of the most w ell
known scientists in India in the field of Vaccum Electronics Devices for over the and half decades. His professional
achievements span a wide area of vaccum microwave devices ranging from crossed -field and linear-beam devices to
present-day gyrotrons. He was awarded a degree of M. Sc (Physics) and M. Sc Tech (Electronics) from BITS Pilani. He
started his professional carrier in 1966 at Central Electronics Engineering Research Institute (CEERI), Pilani. During
this period he designed and developed a specific high power Magnetron for defense and batch produced about 100 tubes
for their use. Trained the Engineers of Industries with know how transfer for further production of the same.
In 1979 he visited department of Electrical and Electronics Engineering at the University of Sheffield (UK) in the capacity
of independent research worker and Engineering Department of Cambridge University Cambridge (UK) as a visiting
scientist. He has an experience of about 38 years in area of research and development in Microwave field with several
papers and a patent to his credit. In 2003 retired as scientist from CEERI, PILANI & shifted to Jaipur and joined the
profession of teaching. From last eight years he is working as professor and head of electronics department in various
engineering colleges. At present he is working as head and Professor in the department of Electronics and communication
engineering at JNU, Jaipur. He has guided several thesis of M.tech .of many Universities.


IJCER (www.ijceronline.com) International Journal of computational Engineering research

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (16)

En vedette

En vedette (8)

Similaire à IJCER (www.ijceronline.com) International Journal of computational Engineering research

Similaire à IJCER (www.ijceronline.com) International Journal of computational Engineering research (20)

Dernier

Dernier (20)

IJCER (www.ijceronline.com) International Journal of computational Engineering research